Asian Journal of the Scholarship of Teaching and Learning

A Feasibility Study on the Use of Large Language Models in Supporting Adaptive Learning

Cheng Keat TAN, Qing Hao NG, Seh Yi Joseph TAN, Yin Ni NG — Thu, 30 Apr 2026 00:00:00 +0800

Large Language Models (LLMs) have gained prominence as adaptive learning tools. Although effective in assessing multiple-choice questions (MCQs), their accuracy and feedback validity remain uncertain. This feasibility study examines the accuracy, validity, and scientific substantiation of LLMs’ responses to expert-generated pharmacology MCQs and assesses the automated generation of MCQs to elucidate their potential roles and limitations in adaptive learning.

Fifty MCQs, designed and validated by academic pharmacists, were used to test the accuracy of four LLMs. Each question was classified according to Bloom’s Taxonomy. Direct prompting was applied to generate responses for each question. Responses were analysed for accuracy, validity of rationale, and the existence and relevance of supporting references. Chi-Square test and Fisher-Freeman-Halton Test were used to evaluate quantitative findings.

Among the four LLMs, ChatGPT-4o achieved the highest accuracy (84%), followed by Google Gemini 1.5 (Gemini) (80%), Microsoft Copilot (Copilot) (72%), and Claude Sonnet 3.5 (Claude) (68%). An answer-rationale discordance and a decline in performance with an increased cognitive complexity, stratified through Bloom’s Taxonomy, were noted across the LLMs. Artificial hallucinations were observed in the study. These limitations underline the challenges of using LLMs in complex, evidence-driven disciplines like pharmacology.

LLMs, as an educational tool, may provide value to adaptive learning. However, limitations in logical reasoning, scientific support, and higher-order thinking highlight the need for cautious adoption. Continuous efforts to validate LLMs with larger, more diverse question banks are also necessary to fully investigate their potential in adaptive learning.

From Compliance to Critical Literacy: Teaching GenAI Translation Ethics through Synthetic Scenarios

Luis Damián MORENO GARCÍA, Tianhao YANG — Thu, 30 Apr 2026 00:00:00 +0800

The rapid integration of Generative AI (GenAI) into the translation industry has outpaced the development of ethical pedagogical frameworks, leaving students ill-equipped to navigate complex human-machine collaborations. This study addresses this gap by proposing a Situated Learning approach to the teaching of GenAI ethics. Grounded in the 2025 TAC Guidelines for the Chinese translation industry, the study evaluates an instructional design that utilises synthetic, scenario-based learning materials.

As part of a Teaching Development Grant project, 72 translation students (BA and MA) engaged with a series of AI-generated ethical dilemmas focusing on transparency, risk management, and intellectual property. The intervention employed instructional scaffolding through immediate feedback loops (Moodle Lessons) followed by reflective inquiry. Results indicate that while the scaffolding effectively solidified surface knowledge of normative constraints, students struggled with ‘grey area’ decision-making, particularly regarding fan translation and copyright. Qualitative analysis shows students’ understanding evolving from passive compliance to active Critical AI Literacy (CAIL) through the intervention. The study concludes that synthetic scenarios are a viable pedagogical tool for fostering moral imagination and supporting the teaching and learning of GenAI Translation Ethics.

Cultivating a Growth Mindset in Higher Education: Implications for Module and Assessment Design

Ziqian ZHOU — Thu, 30 Apr 2026 00:00:00 +0800

American psychologist Carol Dweck famously posited that one’s intelligence or learning abilities are not fixed aspects of individuals; rather, these are highly malleable and can be developed through persistence, effort, amongst other things. Christening her foundational work as the ‘growth mindset’, Dweck’s theory has profoundly influenced the field of educational psychology and shaped the development of educational policies within and outside the United States. While much of the research on the growth mindset has focused on K-12 education, its implications for higher education remain underexplored. In this reflection essay, I introduce the central tenets of Dweck’s important work, describe its benefits, and show how these principles can be integrated into higher education module and assessment design. I also aim to discuss criticisms of Dweck’s work that are found in the literature and sketch several ways of circumventing these difficulties.

Effective Experiential Learning in The Artificial Intelligence Age

Kankana MUKHOPADHYAY, Misty COOK, Soon Fen WONG — Thu, 30 Apr 2026 00:00:00 +0800

This reflection on our teaching of an overseas experiential learning course in the College of Alice & Peter Tan (CAPT) in the last academic year aims to illustrate how experiential learning can address concerns about students’ reliance on the use of artificial intelligence (AI) in higher education. The current discourse on AI-augmented teaching and learning practice in higher education has raised considerable focus on filtering the AI-generated information to translate it into authentic knowledge. Reflecting on our own teaching, we claim that experiential learning can be an effective filter to help students learn in this AI-age education environment. We intentionally redesigned an assignment and thereby introduced a ‘creative disruption’ to the course. Describing the design, we present the findings from systematically analysing students’ work using the programme learning outcomes as the lens. This reflection focuses on one of the significant findings on a crucial learning outcome – critical thinking. The discussion elaborates how it has been achieved in the course. This evidence-based reflective analysis demonstrates that when classroom boundaries are crossed, and epistemic access is thoughtfully broadened, students can cultivate higher-order thinking skills like interpreting and explaining complexities, applying knowledge from the perspectives gained, empathising with the other (culture/community) and increasing self-knowledge.

PLUS – An Academic Mentorship Guide applying Overall Feedback Theories

Maybeline GOH Boon Ling, Ping Ping TAN — Thu, 30 Apr 2026 00:00:00 +0800

As undergraduates embark on their university journey, navigating academic courses is accompanied by a comprehensive pursuit of becoming well-rounded graduates. Despite this, feedback from courses often remains independent of academic mentorship. This paper introduces PLUS, an innovative academic mentorship paradigm designed to enhance students’ academic prowess through personalised guidance. Building upon preliminary works, PLUS serves as a personalised feedback guide supported by feedback models and theories. This reflection focuses on the impact of mentors’ feedback using the PLUS technique, addressing how academic mentors provide personalised guidance and whether students perceive the usefulness of this feedback. This reflection utilises survey methodology involving 20 software engineering students to analyse the impact of PLUS as a feedback guide. The survey results reveal unanimous agreement on its helpfulness and the benefits of mentors’ feedback. The survey highlights the importance of regular check-ins between students and mentors, emphasising the effectiveness of the mentor-student relationship. Respondents noted the motivational aspects of PLUS, appreciating its structured approach to feedback. While pressure to perform well was identified as a weakness, respondents suggested focusing on outcomes rather than grades for improvement. In conclusion, the PLUS approach emerges as a powerful guide to effective feedback in academic mentorship in higher learning, fostering a collaborative and enriching academic environment in line with the empirical model for feedback.